Application of the Rural Development Index to Analysis of Rural Regions in Poland and Slovakia

Abstract

The main purpose of this research was to construct a multi-dimensional (composite) index measuring the overall level of rural development and quality of life in individual rural regions of a given EU country. In the Rural Development Index (RDI) the rural development domains are represented by hundreds of partial socio-economic, environmental, infrastructural and administrative indicators/variables at NUTS-4 level (e.g. 991 variables/indicators describing various aspects of rural development in Poland; 340 variables/indicators in Slovakia). The weights of economic, social and environmental domains entering the RDI index are derived empirically from the econometrically estimated intra- and inter-regional migration function after selecting the “best” model from various alternative model specifications (e.g. panel estimate logistic regression nested error structure model, spatial effect models, etc.). The RDI is empirically applied to analysis of the main determinants of rural/regional development in individual rural areas in years 2002–2005 in Poland and Slovakia at NUTS-4 level. Due to its comprehensiveness the RDI Index is suitable both to analysis of the overall level of development of rural areas and to an evaluation of the impacts (impact indicator) of RD and structural programmes at regional levels (NUTS 2–5).

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Notes

  1. 1.

    Already in the late 1960s dissatisfaction with an abundant usage of GDP and a stringent definition of economic growth led to development of alternative approaches involving a further conceptualization of the quality of life. These trends were followed by efforts aimed at developing a composite index that would embrace various aspects/domains of a quality of life previously largely ignored in a standard GDP per capita measure (Kaufmann et al. 2007; DEFRA 2004). Although a quality of life (QOL) index reflecting various aspects of regional/rural development at regional levels is generally considered as superior, compared to GDP per capita, numerous methodological difficulties linked to construction of such an index (both at country as well as regional levels) have previously prohibited its wider usage.

  2. 2.

    Compared with difficulties experienced by collectors of such indicators two or three decades ago, a plentiful regional statistical data base available for researchers and policy analysts today, enables (at least theoretically) a comprehensive analysis of the development of rural areas by means of hundreds/thousands of various partial indicators calculated at various regional levels, including NUTS-4 and NUTS-5. Increased data availability also fuels the interest of policy makers (including EC) to apply such data in evaluations of EU RD/structural programmes (EC 2006).

  3. 3.

    Implemented RD programmes and policies may lead to simultaneously positive (usually expected by policy makers) and negative (e.g. unexpected general equilibrium) effects. For example, support of investments in rural infrastructure or in processing facilities, along with some positive effects, may bring about negative environmental impacts, including potential loss of land supporting biodiversity, protected habitats and/or species, deterioration of soil, water environment and air quality, etc. Similarly, support of local food processors may lead to negative effects in the form of strengthening local monopolies (e.g. large processors), causing breakdown of other local food processing businesses, and therefore a decrease of employment and income in non-supported local enterprises, an increase out-migration, etc.; some investments in irrigation may cause depletion of water resources in other areas, etc.; support provided to certain types of agricultural producers may have negative effects on on-supported population, etc. In all these cases an assessment of a net-effect (impact) of pursued policies may be rather unmanageable, because positive and negative outcomes expressed in form of partial indicators only hardly can be compared to each other (social weights of individual effects in various RD domains, e.g. economic, social and environmental are usually unknown).

  4. 4.

    Empirical studies show that in many cases a considerable increase in the population’s standard of living has almost no detectable effects on life satisfaction or happiness pronounced in direct interviews, see: Easterlin (1995, 2001), Burkholder (2005), Kahneman and Krueger (2006).

  5. 5.

    E.g. Jones and Riseborough (2002), OSDC (Ontario) (2000), Aivazian (2005), Osberg and Sharpe (2000, 2002), Anderson (2004), Rosner (2002), Douglas and Wall (1993).

  6. 6.

    E.g. Grasso and Canova (2007), Rahman et al. (2005), Sung-Bok (2005).

  7. 7.

    E.g. Krishnakumar (2007), Kuklys (2004), Juanda and Wasrin (2004).

  8. 8.

    E.g. Buettner and Ebertz (2009).

  9. 9.

    E.g. Deller et al. (2001).

  10. 10.

    E.g. Lovell et al. (1994), Zhu (2001), Deutsch et al. (2001).

  11. 11.

    E.g. Rosen (1979), Roback (1982), Gyourko and Tracy (1989), Berger et al. (2003), Gabriel et al. (2003), Wall (1997), Douglas and Wall (2000), Granger (2008).

  12. 12.

    The original foundation for analyzing the effect of regional performance and migration was provided by Tiebout (1956), who found out that, as long as consumers are fully mobile and informed, they convey their preferences through migration or “voting with their feet”. A vast sociologic and economic literature shows that people tend to move in order to improve the quality of their lives in a variety of specific respects, and they continue to move until they achieve goals for the majority of those respects (Fuguitt 1985; Michalos 2003; Berger et al. 2003; Douglas and Wall 1993, 2000).

  13. 13.

    Furthermore, various migrations studies showed empirically that people living in societies that have reached a certain stage of material wealth will also increasingly focus upon immaterial aspects of life, e.g. attractiveness of places that depends upon the needs, demands and preferences of the individual (Inglehart 1997; Niedomysl 2006).

  14. 14.

    In a general theory of movement (Alonso 1978; de Vries et al. 2000) it is argued that the migration flows between locality i and locality j depend not only upon characteristics of the localities of origin and destination, but also upon the ease of movement between them as well as upon the alternative opportunities available from that origin and the degree of competition existing at that destination. An empirical estimation of the Alonso’s simultaneous equation model with unobservables is provided in de Vries et al. (2000).

  15. 15.

    While many of migration models forecast the probability of migration from area i to area j depending on the ratio of various destination-to-origin characteristics describing differentials in the quality of life between both areas, an individual migration decision itself can be modelled as a two-step decision process. First a decision maker decides whether to migrate, based on origin characteristics, and second, a choice of destination area is made based on destination characteristics and by taking into consideration other variables describing transaction costs of migration (e.g. distance between origin and destination areas).

  16. 16.

    In the limit, as the unit of time diminishes over which migration is measured, differences between these two specification of migrations might be expected to diminish (Schultz 1982). The reason is that the population at risk to migrate becomes a better measure of the non-migrating population when the migration interval is very short (Greenwood 1997). In an extension to this approach, i.e. the new economics of labour migration (e.g. Stark 1991; Stark and Bloom 1985), migration decision is modelled in a larger context—typically the household, which usually consists of individuals with different preferences and different access to income and is influenced by its social milieu (Taylor and Martin 2001; Mincer 1978; De Jong et al. 1998; Konseiga 2007).

  17. 17.

    The hypothesis that there is an inverse relationship between the distance between receiving and sending areas and the likelihood of moving was confirmed in number of empirical studies (Jones 1976; Michalos 2003).

  18. 18.

    Arguments for using distance as a proxy for transaction costs of moving between origin and destination regions are as follows (Greenwood 1997): (a) distance reflects costs of breaking important ties with relatives and friends as well as other forces; (b) longer distances between origin and destination areas also usually imply higher information costs to offset the greater uncertainty associated with longer distance locations; (c) usually longer distance require more time which in turn means more foregone earnings if the individual is not explicitly compensated for it, e.g. is not involved in a job transfer; (d) distance may also serve as a proxy for the costs of moving which could be offset by making more frequent or longer trips back to the origin, where each type of return trips raises the costs of moving as a positive function of distance.

  19. 19.

    For each state Greenwood et al. (1991) estimated the per capita income that would be necessary for there to be no net migration to the state from the rest of the country. If this estimated income was less than national average, the state was said to be amenity-rich.

  20. 20.

    The modelling technique applied in Douglas and Wall (2000) allowed the ranking of provinces in terms of their non-pecuniary amenities and to calculate the value of those amenities in terms of their income value, or compensating differential.

  21. 21.

    In Douglas and Wall (1993) data on net migration flows between states was directly used for calculation of a Quality of Life. Construction of QOL ranking was performed by making pair-wise comparisons of migration rates. In Douglas and Wall (2000) the quality of life was estimated as a constant from a net-migration rate function with intercepts (QOL) and income ratio as the main arguments.

  22. 22.

    In Douglas and Wall (2000) the authors distinguish between the concept of the standard of living (SOL) and the quality of life (QOL); for the former includes both QOL and the differences in income. In our concept the differences in income are already included into the overall measure of the quality of life.

  23. 23.

    The search for relevant literature in economics and psychology identified a total of 153 papers linked to the concept of well-being (DEFRA, 2006).

  24. 24.

    Douglas and Wall (1993) show that MR ij is an asymptotically normally distributed variable with mean that depends on the differences: qj − qi − Cij between i and all other possible locations n.

    \( E\left( {{\text{MR}}_{\text{ij}} } \right) = f\left( {{\text{q}}_{\text{j}}-{\text{q}}_{\text{i}}-{\text{C}}_{\text{ij}} , {\text{q}}_{\text{j}}-{\text{q}}_{ 1}-{\text{C}}_{{ 1 {\text{j}}}} ,{\text{q}}_{\text{j}}-{\text{q}}_{ 2}-{\text{C}}_{{ 2 {\text{j}}}} , \ldots } \right) \).

    where E(MRij)  = expected value of a migration rate between regions i and j, and; f = includes all possible alternative destinations (n) for moving of individual l living in region i. It can also be shown that in large samples the probability of migrating from region i to j and from region j to region i will be independent of individual stochastic elements \( \varepsilon_{{\mathbf{i}}}^{{\mathbf{l}}} \) (Eq 8).

  25. 25.

    While in our study RDIs are computed directly using all i-region specific Zki and β, this approach to the construction of a QOL Index differs from one described in Douglas and Wall (1993, 2000) for its explicit estimation of covariates (quality of life determinants and the magnitude of the estimated transaction costs Cij, see: Model (8a, b).

  26. 26.

    This factorization method treats communalities as all 1 meaning that there are no unique factors (extraction of principal components amounts to a variance maximizing rotation of the original variable space, whereby each consecutive factor is defined to maximize the variability that is not captured by the preceding factor. This leads to consecutive factors being uncorrelated or orthogonal to each other.

  27. 27.

    Variables VAR i a are normally directly available (or have to be computed as a coefficient (e.g. per capita) from secondary statistics on individual regions.

  28. 28.

    Above study also includes a complete comparison of results from modelling RDI by applying quantitative approaches described above.

  29. 29.

    Clearly, the weights used later to construct the RDI are only a subset of all coefficients estimated within this specification.

  30. 30.

    For treatment of zero observations see Sect. 8.2.

  31. 31.

    The random effect estimator produces more efficient results than between estimator, albeit with unknown small sample properties. The between estimator is less efficient because it discards the over time information in data in favour of simple means; the random-effects estimator uses both the within and the between information (STATA, ver.10; Kennedy 2003).

  32. 32.

    This suggests a high degree of universalism across different countries in what are considered as social concerns (Johansson 2002).

  33. 33.

    For example, in some quality of life studies representatives of individual regions had chosen indicators that were not necessarily comparable across regions but seemed most appropriate to analysts in the light of their own circumstances and priorities (DEFRA 2004).

  34. 34.

    The list of available regional indicators in Poland can be found under: http://www.stat.gov.pl/bdren_n/app/strona.indeks.

  35. 35.

    Statistical verification of the magnitude and scope of contribution of individual variables/coefficients to the overall rural development (RDI) is one of the outcomes of this study.

  36. 36.

    The principal components are normalized linear functions of the indicator variables and they are mutually orthogonal. The first principal component accounts for the largest proportion of the total variation of all indicator variables. The second principal component accounts for the second largest and so on. To obtain interpretable results the solution was rotated using the Varimax technique (the method minimizes the number of variables with high factor loading values). The resulting structure of factor-loadings comprises information about the impact of single variables on each extracted factor. While both the size as well as the quantity are of importance, rotated loadings were sorted by size. In this way patterns of similarity between individual items (coefficients/variables/indicators) that load on a given factor became straightforward.

  37. 37.

    The application of a principle component method was also favoured because it provides a unique solution, so that the original data can be reconstructed from the results.

  38. 38.

    In order to ensure positivity of the log function, values of migration equal to 0 was replaced with the value 0.00000001.

  39. 39.

    Further methodological improvements, e.g. linking of Model (8) with spatial econometrics, due to a large number of regions, led to problems with data processing (e.g. estimation of W-matrix under a General Spatial Model). While extension of Models 8a and 8b through inclusion of spatial regional interdependencies is theoretically possible this approach was dropped due to computational problems involving processing of the huge amount of spatial data for a large number of regions.

  40. 40.

    While contextual structure of individual factors/principal components in both countries differs, the cut-off applied for interpretation purposes were those variables with the highest factor loadings (positive or negative).

  41. 41.

    In a few cases the same partial coefficient was “assigned” to more than one RD domain (e.g. expenditures for public utilities and environment were assigned to both environmental as well as infrastructural domains).

  42. 42.

    Obviously, positive terms (i.e. positive contribution of a given factor/principal component to the overall level of regional development) can be obtained for regions over-proportionally endowed with factors/principal components that display positive weights. Yet, in case a factor/principal component displays a negative weight (i.e. an increase of this factor leads to diminution of the quality of life) an under-proportional endowment of a given region with this particular factor (negative standardized factor’s value) results also in a positive term (positive contribution to the RDI). In contrary, under-proportional factor endowment with factors with positive weights results in negative terms (negative contribution to rural development). The same applies to an over-proportional endowment of a region with factors exhibiting negative weights (i.e. negative term).

  43. 43.

    These may have occurred as a policy response to a low local development level.

  44. 44.

    As mentioned above, the best performing rural regions in Poland were found to be located close to big cities (e.g. Warsaw, Poznan, Gdansk, Krakow). On the other hand the least developed rural regions were found in remote areas in Eastern Poland (e.g. close to the Belarusian or Ukrainian border) or in post heavy industrial zones (e.g. poviat walbrzyski bordered with the Czech Republic).

  45. 45.

    In Poland the most stable were regions in quartile 1, i.e. the best developed regions (see above).

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Acknowledgments

This study was supported by the European Commission through the 6th Framework Programme for Research and Development. Responsibility for the information and views set out in this article lies entirely with the author.

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Correspondence to Jerzy Michalek.

Annex

Annex

See Fig. 10a, b.

Fig. 10
figure10

a Poland: RDI components sorted by size of weight. b Slovakia: RDI components sorted by size of weight

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Michalek, J., Zarnekow, N. Application of the Rural Development Index to Analysis of Rural Regions in Poland and Slovakia. Soc Indic Res 105, 1–37 (2012). https://doi.org/10.1007/s11205-010-9765-6

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Keywords

  • Composite index
  • Rural development
  • Quality of life
  • Multi-level mixed-effect regression model
  • Evaluation impact indicator